Picture quality and compression analysis of multilevel legendre wavelet transformation based image compression technique

Abstract

A novel lossy RGB (Red, Green, Blue) colour still image compression algorithm is proposed. The intended method introduces Legendre wavelet-based image transformation technique integrated with vector quantization and run length encoding. High performance is guaranteed by lowering degradation in picture quality with desired compression. Transformation (Specifically) and Quantisation (implicitly) phases focus on reducing number of pixel values from pixel set and contribute in attaining higher compression ratio. Out of these two phases of image compression technique, the phase of transformation should be more effective with a view to implement its functionality because the lossless nature of this phase does not perturb the quality of reconstructed image. Image transformation via Legendre wavelet functions, along with self organizing map based quantization, proposed method for scanning of quantized values and run lenght encoding, tends to produce much sparser matrix when measured against Haar wavelet based compression. Due to the combined effect of curvilinearity nature of their component wavelets, the proposed Legendre wavelet based transformation provides comparatively much more higher PSNR of 225(average) with satisfactory compression of 0.41 bits per pixel(average). In this paper, image transformations are conducted using Haar wavelet, Legendre wavelets and transformation method presented in [7]. Experimental results have been analysed and compared in terms of qualitative and quantitative measure which are PSNR (Peak Signal to Noise Ratio) and bpp (bits per pixel). The performance of proposed algorithm is compared with existing Haar wavelet transformation-based image compression algorithm, compression based on transformation method [7], DCT and adaptive scanning based compression [12] and JPEG [5] compression. Picture quality achieved in the experiments clearly show that the proposed Legendre wavelet -oriented image transformation based image compression technique remarkably outperforms the above mentioned compression techniques. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.